The NIMH strategic objectives emphasize the importance of developing strategies for personalized treatment, yet there are currently no widely utilized biomarkers to guide clinicians in the usage of second generation antipsychotic (SGAs). While most SGAs have adequate (though far from perfect) efficacy for the treatment of positive symptoms in schizophrenia, a major concern for patients, families, and clinicians is the burden of antipsychotic-induced weight gain (AIWG). Moreover, the problem of AIWG is a growing public health concern due to the rapidly accelerating use of SGAs for the treatment of non-psychotic conditions in children, adolescents, and adults. Identification of biomarkers predicting AIWG liability are urgently needed for several reasons: 1) the degree of AIWG can be extreme (>14% of baseline weight) in as many as one-fifth of all patients; 2) AIWG can lead to metabolic syndrome and related morbidity; 3) AIWG can be stigmatizing and cause further psychological suffering in patients; and 4) AIWG often leads to medication nonadherence resulting in increased risk of relapse and re-hospitalization. Additionally, the mechanisms underlying AIWG are poorly understood, and the identification of predictive biomarkers can help illuminate the underlying pharmacology, thereby aiding the development of novel medications with reduced AIWG burden. The proposed study aims to identify pharmacogenetic biomarkers for AIWG by analyzing genomewide association study (GWAS) data obtained on 6 cohorts (total n~1200) of prospectively-characterized, SGA- treated patients who are either antipsychotic-nave or have very limited prior exposure. In order to appropriately control for study-specific variables such as study duration, treatment type, and demographics, GWAS (with appropriate covariates) will be performed in each cohort separately, with results combined via meta-analysis. The relationship of genotype to within-subject changes in BMI will be the primary outcome of interest; secondary outcomes will include antipsychotic-induced changes in other metabolic parameters, including triglyceride levels, cholesterol levels, hip and waist circumference, and fat mass, induced by SGA treatment. Note that no funds in the proposed study are used for subject recruitment, data collection, or genotyping. The proposed study builds upon our recent identification of a genetic biomarker that replicably predicts a doubling of AIWG in carriers of the risk genotype (~20 lbs of weight gain in 12 weeks, as compared to ~10 lbs), utilizing a GWAS based on a much smaller sample size (n=139).